Type:	Package
Title:	Transporting Intervention Effects from One Population to Another
Version:	0.1.0
Description:	Doubly-robust, non-parametric estimators for the transported average treatment effect from Rudolph, Williams, Stuart, and Diaz (2023) <doi:10.48550/arXiv.2304.00117> and the intent-to-treatment average treatment effect from Rudolph and van der Laan (2017) <doi:10.1111/rssb.12213>. Estimators are fit using cross-fitting and nuisance parameters are estimated using the Super Learner algorithm.
License:	GPL (≥ 3)
Encoding:	UTF-8
Imports:	checkmate, cli, generics, ife, mlr3superlearner, origami, R6
RoxygenNote:	7.3.2
URL:	https://github.com/nt-williams/transportr
BugReports:	https://github.com/nt-williams/transportr/issues
Suggests:	ranger, testthat (≥ 3.0.0)
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2025-02-19 17:43:26 UTC; nicholaswilliams
Author:	Nicholas Williams [aut, cre, cph], Kara Rudolph [aut, cph]
Maintainer:	Nicholas Williams <ntwilliams.personal@gmail.com>
Repository:	CRAN
Date/Publication:	2025-02-20 18:10:02 UTC

transportr: Transporting Intervention Effects from One Population to Another

Description

Doubly-robust, non-parametric estimators for the transported average treatment effect from Rudolph, Williams, Stuart, and Diaz (2023) doi:10.48550/arXiv.2304.00117 and the intent-to-treatment average treatment effect from Rudolph and van der Laan (2017) doi:10.1111/rssb.12213. Estimators are fit using cross-fitting and nuisance parameters are estimated using the Super Learner algorithm.

Author(s)

Maintainer: Nicholas Williams ntwilliams.personal@gmail.com (ORCID) [copyright holder]

Authors:

• Kara Rudolph kr2854@cumc.columbia.edu (ORCID) [copyright holder]

See Also

Useful links:

• https://github.com/nt-williams/transportr

• Report bugs at https://github.com/nt-williams/transportr/issues

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

generics

tidy

Transported Average Treatment Effect

Description

Implements two one-step estimators for the transported average treatment effect for a binary or continuous outcome. Nuisance parameters are estimated using the Super Learner algorithm.

Usage

transport_ate(
  data,
  trt,
  outcome,
  covar,
  pop,
  obs = NULL,
  id = NULL,
  weights = NULL,
  estimator = c("standard", "collaborative"),
  learners_trt = "glm",
  learners_pop = "glm",
  learners_outcome = "glm",
  learners_heterogeneity = "glm",
  folds = 1,
  control = transport_control()
)

Arguments

Details

Estimators

The "collaborative" estimator uses covariate dimension reduction and does not require users to have knowledge about which covariates are effect modifiers and which differ in distribution between the populations. The "standard" estimator assumes all covariates are effect modifiers and differ in distribution between the populations.

Value

An object of class transported_ate containing the parameter estimate.

Examples

gendata <- function(n, A = NULL) {
  W <- rbinom(n, 1, 0.5)
  V <- rbinom(n, 1, 0.66)
  Z <- rbinom(n, 1, 0.33)

  if (is.null(A)) A <- rbinom(n, 1, 0.5)

  S <- rbinom(n, 1, 0.4 + 0.5*W - 0.3*Z)

  Yi <- rnorm(n, A + W + A*V + 2.5*A*Z, sqrt((0.1 + 0.8*W)^2))
  Y <- ifelse(S == 1, Yi, NA_real_)

  data.frame(W = W,
             V = V,
             Z = Z,
             S = S,
             A = A,
             Y = Y,
             Yi = Yi)
}

set.seed(123)
n <- 250

tmp <- gendata(n)

transport_ate(data = tmp,
              trt = "A",
              outcome = "Y",
              covar = c("W", "V", "Z"),
              pop = "S",
              estimator = "standard",
              folds = 1)

transport_ate(data = tmp,
              trt = "A",
              outcome = "Y",
              covar = c("W", "V", "Z"),
              pop = "S",
              estimator = "collaborative",
              folds = 1)

Set Estimation Parameters

Description

Defines default parameters for estimators in the 'transport' package.

Usage

transport_control(
  .learners_folds = NULL,
  .bound = 1e+05,
  .return_full_fits = FALSE,
  .discrete = FALSE,
  .info = FALSE
)

Arguments

Value

A list with parameter values.

Examples

transport_control(.learners_folds = 10)

Transported Intent-to-Treat Average Treatment Effect

Description

Implements a TMLE for the transported intent-to-treat average treatment effect. Nuisance parameters are estimated using the Super Learner algorithm.

Usage

transport_ittate(
  data,
  instrument,
  trt,
  outcome,
  covar,
  pop,
  obs = NULL,
  id = NULL,
  weights = NULL,
  learners_instrument = "glm",
  learners_trt = "glm",
  learners_pop = "glm",
  learners_outcome = "glm",
  folds = 1,
  control = transport_control()
)

Arguments

Value

An object of class transported_ittate containing the parameter estimate.

Examples

gendata <- function(n, A = NULL, S = NULL) {
  if (is.null(S)) S <- rbinom(n, 1, 0.5)
  W1 <- rbinom(n, 1, 0.4 + (0.2 * S))
  W2 <- rnorm(n, 0.1 * S, 1)
  W3 <- rnorm(n, 1 + (0.2 * S), 1)
  if (is.null(A)) A <- rbinom(n, 1, 0.5)
  Z <- rbinom(n, 1, plogis(-log(1.6) + log(4)*A - log(1.1)*W2 - log(1.3)*W3))
  Yi <- rbinom(n, 1, plogis(log(1.6) + log(1.9)*Z - log(1.3)*W3 - log(1.2)*W1 + log(1.2)*W1*Z))
  Y <- ifelse(S == 1, Yi, NA_real_)

  data.frame(W1 = W1, W2 = W2, W3 = W3,
             S = S,
             A = A,
             Z = Z,
             Y = Y,
             Yi = Yi)
}

set.seed(123)
n <- 1000

tmp <- gendata(n)

if (requireNamespace("ranger", quietly = TRUE)) {
  transport_ittate(data = tmp,
                   trt = "Z",
                   instrument = "A",
                   outcome = "Y",
                   covar = c("W1", "W2", "W3"),
                   pop = "S",
                   folds = 1)
}